Gym
OpenAI's way to create a reinforcement learning environment, used by most RL implementations.
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OpenAI's way to create a reinforcement learning environment, used by most RL implementations.
Last updated
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Gym seeks to create standard environments, enabling better benchmarks to drive research in reinfrocement learning.
An environment contains:
action_space: space object corresponding to valid actions
observation_space: space object corresponding to valid observations
reward_range: corresponding min and max possible rewards
A step in an environment contains:
observation (object): pixel data, angles, velocities or board states
reward (float): amount of reward achieved by previous action
done (boolean): whether to reset the environment, termination action.
info (dict): diagnositic information (i.e. raw probabilities for last state)
How to create custom environment:
Keras-RL uses gym style environments (although the included ones are )
